Frontiers in immunology · 2026
Untargeted serum metabolomics in systemic lupus erythematosus reveals candidate metabolomic signatures associated with clinical phenotypes and glucocorticoid use.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Eades Laura E, Barlow Christopher K, Jeppe Katherine J, Valipour Kahrood Hossein, Schittenhelm Ralf B, Kent Joanna R, Kitching Arthur Richard, Papadaki Artemis, Figgett William A, Kandane-Rathnayake Rangi, Hoi Alberta Y, Morand Eric F, Vincent Fabien B
Аннотация
OBJECTIVES: Despite promising advances in metabolomics for biomarker discovery in systemic lupus erythematosus (SLE), studies examining associations with detailed disease phenotypes, including organ damage and validated outcome measures, remain limited. We aimed to explore associations between serum metabolomic profiles with SLE phenotypes.
METHODS: Untargeted serum metabolomics was performed in 60 SLE patients using liquid chromatography-mass spectrometry. Metabolites with missing values or low-confidence assignments were excluded. Differential abundance analysis (DA) using regression models and supervised learning using least absolute shrinkage and selection operator (LASSO) were performed to investigate relationships between metabolites and clinical features. Covariates in multivariable models included age, sex, ethnicity and renal impairment (eGFR < 60 mL/min/m2). Disease activity was assessed using the SLE Disease Activity Index-2000 (SLEDAI-2K), with renal activity defined as the presence of SLEDAI-2K proteinuria.
RESULTS: 364 metabolites were studied in 58 patients (median (IQR) age 42 (34-52) years, 91% female, 50% Asian ethnicity, 50% European ethnicity, 50% active disease). Using DA, 20 metabolites were significantly associated with glucocorticoid use, although no predictive LASSO model for this feature was identified. A metabolomic feature putatively identified as 5β-Cholestane-3α, 7α, 24, 25-tetrol was significantly negatively associated with biopsy-proven lupus nephritis (LN) and active LN. Glutamic acid was significantly positively associated with glucocorticoid-free remission. Homocitrulline and N6, N6, N6-trimethyl-L-lysine were significantly positively associated with active LN. A feature putatively identified as 3-Hydroxy-N6, N6, N6-trimethyl-L-lysine was significantly positively associated with biopsy-proven LN. These metabolite associations were also predictive on LASSO modelling. Pathway-level enrichment revealed associations with active disease (Valine, Leucine and Isoleucine Degradation, FDR = 0.0015), organ damage (Citric Acid Cycle and Gluconeogenesis, FDR = 0.043 each), DORIS remission (Phenylalanine and Tyrosine Metabolism, FDR = 0.028; Valine, Leucine and Isoleucine Degradation, FDR = 0.041), and remission off prednisolone (Urea Cycle, Tryptophan Metabolism, Purine Metabolism, and Glutamate Metabolism; FDR = 0.044 each).
CONCLUSIONS: Associations between glucocorticoid use and serum metabolites highlight the importance of accounting for this in SLE metabolomics studies. Significant associations between serum metabolites and SLE phenotypes, particularly previously unreported associations between methylated and carbamylated lysine derivatives with LN, support the promise of serum metabolomics to identify prognostic SLE biomarkers.
METHODS: Untargeted serum metabolomics was performed in 60 SLE patients using liquid chromatography-mass spectrometry. Metabolites with missing values or low-confidence assignments were excluded. Differential abundance analysis (DA) using regression models and supervised learning using least absolute shrinkage and selection operator (LASSO) were performed to investigate relationships between metabolites and clinical features. Covariates in multivariable models included age, sex, ethnicity and renal impairment (eGFR < 60 mL/min/m2). Disease activity was assessed using the SLE Disease Activity Index-2000 (SLEDAI-2K), with renal activity defined as the presence of SLEDAI-2K proteinuria.
RESULTS: 364 metabolites were studied in 58 patients (median (IQR) age 42 (34-52) years, 91% female, 50% Asian ethnicity, 50% European ethnicity, 50% active disease). Using DA, 20 metabolites were significantly associated with glucocorticoid use, although no predictive LASSO model for this feature was identified. A metabolomic feature putatively identified as 5β-Cholestane-3α, 7α, 24, 25-tetrol was significantly negatively associated with biopsy-proven lupus nephritis (LN) and active LN. Glutamic acid was significantly positively associated with glucocorticoid-free remission. Homocitrulline and N6, N6, N6-trimethyl-L-lysine were significantly positively associated with active LN. A feature putatively identified as 3-Hydroxy-N6, N6, N6-trimethyl-L-lysine was significantly positively associated with biopsy-proven LN. These metabolite associations were also predictive on LASSO modelling. Pathway-level enrichment revealed associations with active disease (Valine, Leucine and Isoleucine Degradation, FDR = 0.0015), organ damage (Citric Acid Cycle and Gluconeogenesis, FDR = 0.043 each), DORIS remission (Phenylalanine and Tyrosine Metabolism, FDR = 0.028; Valine, Leucine and Isoleucine Degradation, FDR = 0.041), and remission off prednisolone (Urea Cycle, Tryptophan Metabolism, Purine Metabolism, and Glutamate Metabolism; FDR = 0.044 each).
CONCLUSIONS: Associations between glucocorticoid use and serum metabolites highlight the importance of accounting for this in SLE metabolomics studies. Significant associations between serum metabolites and SLE phenotypes, particularly previously unreported associations between methylated and carbamylated lysine derivatives with LN, support the promise of serum metabolomics to identify prognostic SLE biomarkers.